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Summary and Collect useful function in R

Created by Safe

1. Basic Operation

We can create variable by using <- it mean assign value (right) to variable (left)

# create variable
x <- 1
y <- 2
x+y
print(x + y)
z <- x+y
z

2. Data Types

2.1. Basic Data Type

There are 3 types: Numeric, Logical, Character

varA <- 100     # Numeric ex 1, 1.0
varB <- TRUE    # Logical: TRUE, FALSE
varC <- "Hello" # Character

2.2 Factor

sample <- c("M","F","M","F","F")
gender <- factor(sample)
summary(gender)

2.3. Data Structure

vec1 <- c()             # Vector
list <- list()          # List
world <- data.frame()   # Data Frame

2.4. Useful Function

help(name)        # Help Document
length(object)    # number of elements or components
str(object)       # structure of an object
class(object)     # class or type of an object
summary(object)   # result summaries

3. Descriptive Statistics

  • mean(), median(), min(), max(), quartile(), range(), sd(), var(), iqr()
  • mean(nums,na.rm = TRUE) to handle NA (Not Available’ / Missing Values) Value
# Finding mode
library(DescTools)
Mode()

4. Explore the datasets in base R

4.1. Loading Data

data()
read.csv("filename")

# readr package
library(readr)      # A fast and friendly way to read rectangular data
read_csv("filename")
write_csv(data,file = "filename")

4.2. Useful Function

View(obj)   # Invoke a Data Viewer
head()      # See 6 observation (rows)
str()       # Structure of object
colnames()  # Column Name
rownames()  # Row Name
colMeans()  # Mean each column
rowMeans()  # Mean each row
colSums()   # Sum each column
rowSums()   # Sum each row
table()     # Cross Tabulation and Table Creation

mtcars[1,]      # Select first row with all column
mtcars[,1]      # Select all row with only first column

5. Packages

install.packages(name)      # Install Package
library(name)               # Loading/Attaching and Listing of Packages
help(package=name)          # Read description
  • dplyr: data manipulation
  • tidyr: help you create tidy data
  • readr: A fast and friendly way to read rectangular data
  • stringr: Character manipulation
  • assertive: Readable check functions to ensure code integrity
  • lubridate: Handle datetime format

6. dplyr Package

library(dplyr)

6.1 Function

cars <- as_tibble(mtcars)   # enhanced version of data.frames
glimpse(cars)               # print the data similar str() function
cars %>% head()             # Using The Pipes Operator in R

6.2 Data Manipulation and Transformation

select()        # picks variables based on their names.
filter()        # picks cases based on their values.
arrange()       #  changes the ordering of the rows.
group_by()      # takes a data frame and one or more variables to group by
summarise()     # reduces multiple values down to a single summary.
mutate()        # adds new variables that are functions of existing variables
transmute()     # adds new variables and drops existing ones.
rename()        # renaming columns
count()         # Count observations by group

For example

starwars %>% select(name,height)
starwars %>% filter(sex == "male",skin_color == "light")
starwars %>% arrange(height)
starwars %>% summarise(height = mean(height, na.rm = TRUE))
starwars %>% group_by(sex) %>% select(height) %>% summarise(avg = mean(height, na.rm = TRUE))
starwars %>% rename(hair=hair_color)
starwars %>% mutate(height_m = height / 100)
starwars %>% transmute(height_m = height / 100)

7. Checking and Changing the Type of Value

library(assertive)  # Readable check functions to ensure code integrity

7.1. Checking the types of values

  • Logical checking: returns TRUE or FALSE
  • assertive checking: errors when FALSE (using assertive package)
# Logical checking
is.character()
is.numeric()
is.logical()
is.factor()
is.Date()
is.na()

# assertive
assert_is_character()
assert_is_numeric()
...

7.2. Changing the types of values

as.character()
as.factor()
as.numeric()

8. Handling String with stringr

library(stringr)    # Character manipulation

str_trim(string)                          # Trim whitespace from a string
str_remove(string, pattern)               # Remove matched patterns in a string
str_split(string)                         # Split up a string into pieces
str_sub(string, start = 1L, end = -1L)    # Extract and replace substrings from a character vector

9. Removing duplicate data

duplicated()      # return a logical vector, if duplicate will return TRUE.
distinct()        # Select only unique/distinct rows from a data frame.

Using with dplyr package

products %>% duplicated() %>% sum()
products %>% distinct()

10. Handling missing values

na.omit()

11. Outliers data & Range Values

replace(col, condition, replacement)                          # Replace value with condition
assert_all_are_in_closed_range(col, lower = l1, upper = u1)   # Check in range of [l1,u1]